System and method for dynamic SKU generation in distribution management

Abstract

To provide a system and method for automated stock keeping unit (SKU) management.SOLUTION: A system 1100 includes a user interface for receiving diverse catalog files, a catalog transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a search platform. The catalog transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a dynamic SKU creation module and a global pricing engine for real-time pricing, to improve data accuracy and SKU management, thereby facilitating order processing.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 513,078, filed July 11, 2023, and U.S. Provisional Patent Application No. 63 / 513,078, filed July 21, 2023, each of which is incorporated by reference in its entirety.

[0002] The disclosed embodiments relate to aspects of a user interface (UI) method and system. The traditional global distribution industry faces numerous challenges including supply chain management, inventory control, stock keeping unit (SKU) management, compliance, and evolving consumer expectations.

[0003] Managing SKUs has so far presented numerous challenges that hinder operational efficiency, scalability, and customer satisfaction. Existing platforms and systems struggle to address these complexities, leading to manual processes, data inconsistencies, and scalability limitations. The IT distribution industry faces a major challenge in dealing with the sheer number and variety of SKUs. The process of creating, categorizing, and pricing these SKUs involves significant manual intervention, leading to errors, inconsistencies, and delays. Such inefficiencies not only consume valuable time and resources, but also hinder efficient handling of large volumes of SKUs. In addition, existing platforms lack streamlined processes and self-service capabilities, creating further difficulties in collaborating with vendors and leading to delays, inaccuracies, and inefficiencies in synchronizing product data across platforms.

[0004] Scalability has become another key challenge for IT distribution platforms, especially when dealing with a wide range of vendors offering diverse products, including long-tail and mid-tail suppliers. Maintaining data integrity and ensuring quality control in SKU management is paramount. However, current platforms often fall short in ensuring accurate and consistent data, resulting in internal operational challenges and subpar customer experience. Inconsistent category classification, attribute mapping errors, and data inconsistencies hinder customers from finding and comparing products due to inconsistent and inaccurate data across platforms.

[0005] Pricing SKUs adds complexity when factors such as special pricing, margin rules, and real-time market conditions are considered. Existing platforms lack an efficient mechanism for calculating and updating prices, leading to delays, inaccuracies, and lost revenue opportunities. Manual price management processes exacerbate the problem, resulting in inefficiencies, errors, and potential lost revenue. Furthermore, the concept of virtual SKUs complicates SKU management in the IT distribution domain. Virtual SKUs represent products that are available for customer viewing but have not yet been fully processed. Transitioning virtual SKUs to real SKUs at the time of order requires integration, real-time updates, and efficient back-end processes. However, current systems struggle to smoothly handle this dynamic transition, leading to delays, data inconsistencies, and a poor customer experience.

[0006] Additionally, legacy systems and aging architectures pose significant operational challenges. Diverse data formats, types adopted by different vendors, and improper handling of legacy systems add complexity and impede integration. As a result, SKU management platforms struggle to efficiently process, transform, and validate vendor catalogs, resulting in delays, errors, and operational inefficiencies. There is a need for a real-time, synchronic solution that can efficiently handle SKU generation, pricing, and integration with vendors. Summary of the Invention

[0007] The global distribution industry is at a critical juncture, grappling with a series of challenges across multiple domains. These obstacles are both historical and emerging, requiring the devising of innovative and effective solutions to propel the sector towards growth and efficiency. Among these numerous difficulties, the most significant exist in the areas of supply chain management, inventory and compliance issues, SKU management, the shift to a direct-to-consumer model, and rapidly evolving consumer expectations and behaviors.

[0008] In some embodiments, a real-time and synchronous solution for SKU generation in a distribution platform is provided. Distribution and supply chain management has historically been outside of distributors' core competencies, leading to inefficiencies due to difficulties in dealing with disruptions. Market forces are increasingly favoring a direct-to-consumer model, necessitating a reevaluation of existing business strategies to align with this dynamic change.

[0009] Inventory management poses another inherent problem in distribution. The volatile nature of market demand requires a flexible distribution and supply chain, and the complexity of distribution models including cloud services, everything as a service (XaaS), etc. adds further complexity to the process.

[0010] Further complicating matters are issues surrounding product localization, varying distribution rights, and global SKU management. Reconciling data from various OEMs, each with their own systems and processes, introduces complexity, while complying with requirements in different jurisdictions adds inefficiencies and potential for errors.

[0011] Additionally, the evolving landscape of consumer behavior and expectations necessitates the creation of a user-friendly, efficient, and configurable platform for technology purchases. Traditional ways of interacting with customers are being replaced by the demand for real-time and synchronous interactions, forcing companies to evolve to meet these new customer expectations.

[0012] Despite these challenges, the distribution model holds numerous advantages over the direct-to-consumer model. Specialized entities handling logistics and distribution allow manufacturers to focus on their core competencies. An extensive distribution network reaches customers in remote locations, and value-added services enhance the overall customer experience. To realize these benefits and sustain the distribution model effectively, it is imperative to incorporate real-time and synchronous SKU generation solutions. Addressing the current pain points and streamlining the process through cutting-edge technology will ensure the sustainability and competitiveness of the distribution model.

[0013] Single Pane of Glass The Single Pane of Glass (SPoG) disclosed herein can provide a comprehensive solution aimed at addressing these challenges with a real-time and synchronous approach for SKU generation, providing a holistic, user-friendly and efficient platform that streamlines the distribution process, improving supply chain visibility and inventory management.

[0014] By incorporating real-time tracking and analytics, SPoG provides valuable insight into inventory levels and product status, ensuring supply chain management is efficiently addressed in real-time. By integrating multiple communication channels into a single platform, SPoG emulates direct consumer channels into a distribution platform, improving the overall customer experience through synchronous interactions.

[0015] SPoG's advanced forecasting capabilities provide innovative solutions for improved inventory management through real-time predictive analytics. These forecasts highlight demand trends and guide companies to mitigate the risk of stock-outs or overstocks in real-time.

[0016] The platform further includes a real-time global distribution database, enabling distributors to stay abreast of the latest international regulations in real-time, a feature that reduces the burden of manual tracking and facilitates compliant and delay-free trade across borders.

[0017] SKU management and product localization will be streamlined through consolidation of data from various OEMs by SPoG into a single platform, ensuring data consistency and reducing the chances of errors in real-time.The platform's highly configurable and user-friendly interface provides real-time access to technology in line with the expectations of the new generation technology buyers.

[0018] SPoG's flexible and scalable design ensures it remains a future-proof solution, adapting to changing business needs without major infrastructure changes, thus meeting the demand for real-time and synchronous interactions in dynamic distribution environments.

[0019] Real-time Data Mesh (RTDM) The implementation of Real-Time Data Mesh (RTDM) on the platform provides an innovative solution to address the need for real-time and synchronous SKU generation capabilities in the distribution domain. RTDM provides a distributed data architecture that enables real-time data availability across multiple sources and touchpoints.

[0020] RTDM empowers predictive analytics and provides real-time solutions for efficient inventory control. Foresight into demand trends helps businesses manage inventory in sync with market fluctuations and reduces the risk of overstocking or out-of-stocking in real time.

[0021] Global distribution and compliance is facilitated through real-time updates provided by RTDM, giving distributors the assurance of real-time change and SKU management in addition to maintaining up-to-date compliance. The platform significantly reduces the burden of manual tracking and facilitates delay-free trade across borders.

[0022] RTDM integration of data from different OEMs simplifies SKU management and localization, ensures data consistency and reduces the possibility of errors, and also meets the demand for real-time synchronized transactions.

[0023] By enhancing customer experience, RTDM aggregates and synchronizes data in an intuitive interface, enabling easy access to technology and real-time transactions, meeting the technology partner expectations of a consumer-driven generation.

[0024] Benefits of SPoG and RTDM Integration The integration of SPoG UI platform with RTDM enables a converged and holistic approach to the technical challenges encountered in distribution platforms with a focus on real-time and synchronous SKU generation. SPoG leverages the capabilities of RTDM to improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management and deliver a superior customer experience.

[0025] RTDM's real-time tracking and analytics will significantly improve SPoG's ability to effectively manage its supply chain and inventory, providing accurate, up-to-date information for informed real-time decision-making.

[0026] The integration of SPoG with RTDM ensures synchronous data consistency, reducing errors and delays in SKU management and pricing. A centralized platform for managing data from various OEMs simplifies product localization and aligns with market needs in real-time. This integration highlights the groundbreaking aspect of real-time / synchronous SKU generation in a distribution platform and stands out as a novel and innovative solution for the evolving global market. [Brief description of the drawings]

[0027] [Figure 1] Illustrated is one embodiment of an operating environment for a distribution platform, referred to in this embodiment as a system. [Diagram 2] 2 illustrates one embodiment of a distribution platform operating environment that builds upon the elements introduced in FIG. 1. [Diagram 3] 1 illustrates an embodiment of a system for supply chain management. [Figure 4] 1 depicts an embodiment of an advanced distribution platform that includes a system for managing a complex distribution network, which can be an embodiment of a system, providing a technology distribution platform for optimizing the management and operation of a distribution network. [Diagram 5] 1 illustrates an RTDM module according to one embodiment. [Figure 6] 1 illustrates a SPoG UI according to one embodiment. [Figure 7] FIG. 1 is a flow diagram of a method for performing comprehensive supply chain management operations using a SPoG UI, according to some embodiments of the present disclosure. [Figure 8] FIG. 2 is a flow diagram of a method for vendor onboarding using SPoG UI according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram of a method for reseller onboarding using a SPoG UI, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram of a method for customer and end customer onboarding using a SPoG UI, according to some embodiments of the present disclosure. [Figure 11] 1 depicts an embodiment of a system for managing SKUs in a distribution network, according to some embodiments. [Figure 12] FIG. 2 is a flow diagram of a method for managing SKUs in a distribution network according to some embodiments of the present disclosure. [Figure 13] FIG. 2 is a block diagram of example components of a device according to some embodiments of the present disclosure. [Fig. 14A-14Q] Renders various screens and functionality of the SPoG UI, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] The present embodiments may be implemented in hardware, firmware, software, or any combination thereof. The present embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, and others. Furthermore, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such description is merely for convenience and that such actions are in fact the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0029] It should be understood that the acts shown in the example methods are not exhaustive and that other acts can be performed before, after, or between any of the acts shown. In some embodiments of the present disclosure, acts can be performed in different orders and / or variations.

[0030] 1 illustrates a distribution platform operating environment 100, referred to in this embodiment as system 110. System 110 operates within the context of an information technology (IT) distribution model and serves the needs of various participants, such as customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0031] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions that meet their specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solution based on their requirements. Customers can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0032] End customers 130 are the ultimate beneficiaries of the IT solutions provided by the system 110. They may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on the system 110 to access a wide range of IT solutions, ensuring access to the latest technologies and innovations in the market. The system 110 allows end customers to track their orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0033] Vendors 140 play a key role within the operating environment of system 110. These vendors encompass manufacturers, distributors, and suppliers that offer a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their products, manage inventory, and facilitate business with customers and resellers. Vendors can leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0034] Resellers 150 are the middlemen in the distribution model, bridging the gap between vendors and customers. They play a key role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. The system 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to customers. By leveraging the system 110, resellers can improve relationships with their customers, optimize their product offerings, and increase revenue streams.

[0035] Within the operating environment of the system 110, there are various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system 110 ensures that relevant data is exchanged in real-time between the parties, enabling accurate decision-making and timely actions. Integration with existing enterprise systems, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, warehouse management systems, etc., allows for unified communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0036] Scalability and flexibility are key attributes of the system 110. It can accommodate the growing demands of the IT distribution model, whether that be with a growing customer base, an increasing number of vendors, or a broad range of IT products and services. The system 110 is designed to handle large-scale data processing, storage, and analysis, ensuring that it can support the evolving needs of the distribution platform. In addition, the system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies to provide a robust foundation for its operations.

[0037] In summary, the operating environment of the system 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. The system 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these parties. By leveraging real-time data exchange, integration, scalability, and flexibility, the system 110 empowers the parties to optimize their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0038] Figure 2 illustrates a distribution platform operating environment 200 built from the elements introduced in Figure 1. Within this operating environment, integration points 210 facilitate integrated data flows and connections between various customer systems 220, ancillary systems 230, vendor systems 240, reseller systems 250, and other entities involved in the distribution process. The diagram illustrates the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision making.

[0039] The operating environment 200 can include the system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 can be configured to function and operate as a bridge between the customer system 220, the vendor system 240, the reseller system 250, and other entities within the ecosystem. The communication, data exchange, and transaction process can be integrated to provide a unified and streamlined experience for the participants. Additionally, the operating environment 200 can include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include the following:

[0040] Customer system integration: Integration points 210 can enable system 110 to connect with customer systems 220, allowing for efficient data exchange and synchronization. Customer systems 220 can include various entities, such as customer system 221, customer system 222, customer system 223. Integration with customer systems 220 empowers customers with access to real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0041] Ancillary System Integration: Integration points 210 can enable system 110 to connect with ancillary systems 230, allowing for efficient data exchange and synchronization. Ancillary systems 230 can include various entities, such as ancillary systems 231, ancillary systems 233, and ancillary systems 233. Integration with ancillary systems 220 empowers customers with access to real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0042] Vendor System Integration: Integration point 210 facilitates an integration connection between system 110 and vendor system 240. Vendor system 240 may include entities representing inventory management systems, pricing systems, and product catalogs employed by a vendor, e.g., vendor system 241, vendor system 242, vendor system 243. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0043] Reseller System Integration: Integration point 210 provides the functionality for reseller systems 250 to connect to system 110. Reseller systems 250 may include entities representing sales systems, customer management systems, and service distribution platforms employed by a reseller, such as reseller system 251, reseller system 252, and reseller system 253. Integration with reseller system 250 empowers the reseller to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0044] Other Entity System Integration: Integration point 210 further enables connectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures unified communication and data exchange, facilitating collaboration and an efficient distribution process.

[0045] The integration point 210 also allows for connectivity with systems of record 280 for additional data management and integration. Representing systems of record 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both legacy ERP systems, e.g., SAP, Impulse, META, I-SCALA, and others, as well as future systems. The systems of record can include one or more storage repositories of critical and legacy business data. This facilitates integration of data exchange and synchronization between the distribution platform, the system 110, and the ERP, allowing real-time updates and ensuring availability of accurate and up-to-date information. The integration point 210 establishes connectivity between the systems of record 280 and the distribution platform, allowing stakeholders to leverage the wealth of data stored in the ERP for efficient collaboration, data-driven decision making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0046] Integration points 210 within the operating environment 200 are facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. The system 110 uses industry standard protocols, e.g., RESTful API, SOAP, or GraphQL, to establish communication channels and enable integrated data exchange.

[0047] In some embodiments, the system 110 may incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Token (JWT) may be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0048] In some embodiments, integration points 210 and data flows within operating environment 200 enable participants to operate within a connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows efficiently between customer systems 220, vendor systems 240, reseller systems 250, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision making, and improves operational efficiency throughout the distribution platform.

[0049] In some embodiments, the system 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies to support the integration points 210 and enable unified communication within the operating environment 200. These technologies provide a robust foundation for the system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, the integration points 210 can also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize the distribution process, and personalize the customer experience. The integration points 210 and data flows within the operating environment 200 enable the participants to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows efficiently between different entities, systems, and components. The unified data is processed, harmonized, and provided in real-time through the system 110 to the appropriate participants. This real-time access to accurate and up-to-date information empowers the participants to make informed decisions, optimize supply chain operations, and improve customer experience.

[0050] Some elements in the operating environment depicted in Figure 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, a workstation, a laptop, a PDA, a mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. Each of the customer systems is typically capable of running an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, and the customer systems are capable of accessing, processing, and displaying information, pages, and applications available from the distribution platform over a network.

[0051] Additionally, each of the customer systems may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the customer systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0052] The customer system and its components may be operator configurable using applications including a web browser running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processor units.

[0053] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and systems of other entities to interact with and process web pages, applications, and other data can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as a ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic card, optical card, nanosystem, or any suitable medium for storing instructions and data.

[0054] Furthermore, computer code for implementing the present embodiments can be transmitted and downloaded from a software source over the Internet or any other conventional network connection using communications media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, and others.

[0055] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0056] This enables the operating environment 200 to couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0057] 3 illustrates a system 300 for supply chain management. System 300 is a supply chain management solution designed to address the challenges faced by a fragmented supply chain ecosystem in the global distribution industry. System 300 can include several interconnected components and modules working in harmony to optimize supply chain operations, improve collaboration, and drive business efficiency.

[0058] In some embodiments, the SPoG UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific roles and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI 305 allows users to access relevant information and tools, empowering them to make data-driven decisions and efficiently manage supply chain activities.

[0059] For example, logistics managers can use the SPoG UI305 to monitor shipment status, track delivery routes, and see real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts and graphs, such as a map showing the current location of each shipment, or a bar graph showing inventory levels by product category. By having a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0060] In some embodiments, the SPoG UI 305 is integrated with other modules of the system 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflow. Through API integration, data synchronization mechanisms, and an event-driven architecture, the SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the supply chain ecosystem.

[0061] For example, when a purchase order is generated in the SPoG UI 305, the system 300 automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0062] In some embodiments, a real-time data mesh (RTDM) module 310 can be configured to provide an integrated flow of data within a supply chain ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real-time.

[0063] In one non-limiting example, the RTDM module 310 can collect data from systems of record 280, which can represent a variety of systems including disparate inventory management systems, point of sale terminals, or customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and reconciling discrepancies. The harmonized data is then made available in real time, allowing stakeholders across the supply chain to access accurate, up-to-date information.

[0064] In some embodiments, the RTDM module 310 can be configured to capture data changes across multiple transactional systems in real time. It employs advanced change data capture (CDC) mechanisms that constantly monitor transactional systems to detect updates or changes. The CDC component is specifically designed to work with a variety of transactional systems, which may include future and legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for businesses in diverse environments.

[0065] By providing continuous access to real-time data, stakeholders can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module 310 detects a sudden spike in demand for a particular product, it can trigger an alert to the production team so that they can adjust production schedules to prevent stockouts.

[0066] In some embodiments, the RTDM module 310 facilitates data management within supply chain operations, enabling real-time reconciliation of data from multiple sources and freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, improved customer service, and increased innovation.

[0067] The system 300 can also include an Advanced Analytics and Machine Learning (AAML) module 315. The AAML module 315 can leverage powerful analytics tools and algorithms, such as Apache Spark, TensorFlow, or scikit-learn, to extract valuable insights from the collected data. It performs advanced analytics, predictive modeling, anomaly detection, and other machine learning operations.

[0068] For example, the AAML module 315 can analyze historical sales data to identify seasonal patterns and predict future demand. Forecasts can be generated that help optimize inventory levels, ensure inventory availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module 315 automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0069] In addition to forecasting demand, the AAML module 315 can provide insight into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0070] Additionally, the AAML module 315 can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of consumer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0071] The system 300 can perform integration and interoperability functions to connect with existing enterprise systems, e.g., ERP systems, warehouse management systems, customer relationship management systems. By establishing connections and data flows between these systems, the system 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate unified communication and interoperability between different modules and components, creating a holistic and connected supply chain ecosystem.

[0072] The implementation and deployment of system 300 can be tailored to meet specific business needs. In some non-limiting examples, it can be deployed as a cloud-native solution using containerization technologies such as Docker® and orchestration frameworks such as Kubernetes®. This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0073] The system 300 for supply chain management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented supply chain ecosystem. It combines the power of the SPoG UI 305, RTDM module 310, and AAML module 315 along with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-limiting and can be customized to meet specific industry requirements to achieve efficiency and success in supply chain management.

[0074] FIG. 4 illustrates an embodiment of an advanced distribution platform including a system 400 for managing a complex distribution network, which may be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of a distribution network. System 400 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 405, a customer interaction module (CIM) 410, a RTDM module 415, an AI module 420, an interface display module 425, a personalized interaction module 430, a document hub 435, a catalog management module 440, a performance and prospect marker display 445, a predictive analytics module 450, a recommendation system module 455, a notification module 460, a self-onboarding module 465, and a communication module 470.

[0075] System 400, as one embodiment of system 300, utilizes a wide range of technologies and algorithms to integrate and consolidate supply chain management. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance metrics.

[0076] In some embodiments, the SPoG UI 405 serves as a central interface within the system 400, providing stakeholders with a unified view of the entire distribution network. Front-end technologies such as ReactJS, TypeScript, Node.js, etc. are utilized to create an interactive and responsive user interface. These technologies enable the SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0077] In some embodiments, CIM 410, the customer interaction module, employs algorithms and technologies such as Oracle® Eloqua®, Adobe® Target, and Okta® to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide unified access control to stakeholders.

[0078] In some embodiments, the RTDM module 415, i.e., real-time data mesh module, is a key component of the system 400 and ensures smooth data flow across the supply chain ecosystem. For data ingestion, processing, and stream management, it utilizes technologies such as Apache® Kafka®, Apache® Flink®, and Apache® Pulsar. These technologies enable the RTDM module 415 to handle real-time data streams, process large volumes of data, and ensure low latency data processing. In addition, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems, e.g., legacy ERP and CRM systems. This functionality allows stakeholders to access the latest and accurate information to make informed decisions.

[0079] In some embodiments, the AI ​​module 420 within the system 400 leverages advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn, to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 420 can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize out-of-stock and overstock situations.

[0080] In some embodiments, the interface display module 425 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks such as ReactJS to create interactive and responsive user interfaces. These technologies allow stakeholders to visualize data using various data visualization techniques, e.g., graphs, charts, and tables, allowing for efficient data understanding, comparison, and trend analysis.

[0081] In some embodiments, the personalized interaction module 430 utilizes customer data, trend history, and machine learning algorithms to generate personalized recommendations for products or services. In some non-limiting examples, it can be implemented using Adobe® Target, Apache® Spark, and TensorFlow® for data analysis, modeling, and delivery of targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations to improve customer satisfaction and drive sales.

[0082] In some embodiments, document hub 435 serves as a central repository for storing and managing documents within system 400. In some non-limiting examples, it can be implemented utilizing SeeBurger® and Elastic Cloud for efficient document management, storage, and retrieval. For example, document hub 435 can use SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, compliance documents, etc., allowing stakeholders to easily access and search for relevant documents as needed.

[0083] In some embodiments, the catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. This ensures that stakeholders have access to the latest product information, including specifications, pricing, availability, and promotions. In some non-limiting examples, it can be implemented using Kentico® and Akamai® to consolidate and aggregate catalog updates, content delivery, and caching. For example, the module can leverage Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to stakeholders regardless of geographic location.

[0084] In some embodiments, the performance and outlook marker display 445 collects, analyzes, and visualizes real-time performance metrics and outlook related to supply chain operations. It utilizes tools such as Splunk® and Datadog® to enable effective performance monitoring and provide actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain and enable stakeholders to take proactive measures to optimize operations.

[0085] In some embodiments, predictive analytics module 450 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze historical sales data and forecast future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0086] In some embodiments, the recommendation system module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. In some non-limiting examples, it can be implemented using Adobe® Target and Apache® Spark to analyze data, model, and deliver targeted recommendations. For example, the module can leverage Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels to improve customer engagement and drive sales.

[0087] In some embodiments, the notification module 460 enables delivery of real-time notifications to participants regarding important events, updates, or alerts within the supply chain. In some non-limiting examples, it can be implemented using message queues, event-driven architecture, and Apigee® X and TIBCO® for unified notification delivery. For example, the module can utilize TIBCO's messaging infrastructure to send notifications to participants' devices in real-time to ensure timely distribution of relevant information.

[0088] In some embodiments, the self-onboarding module 465 facilitates the onboarding process for new parties entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its features. In some non-limiting examples, it can be implemented using technologies such as Okta® and Kentico® to ensure secure user authentication, access control, and self-learning resources. For example, the module can utilize Okta's identity and access management capabilities to securely onboard new parties, grant them appropriate access permissions, and guide them through the system's functionality.

[0089] In some embodiments, the communications module 470 enables unified and aggregated communication and collaboration within the system 400. It provides channels for participants to interact, exchange messages, share documents, and collaborate on projects. In some non-limiting examples, it can be implemented using Apigee® Edge and Adobe® Launch to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage API management capabilities of Apigee Edge to ensure secure and reliable communication between participants to enable effective collaboration.

[0090] This allows the system 400 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain management. These modules include SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, catalog management module 440, performance and prospect marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-onboarding module 465, and communication module 470, which work together to provide end-to-end visibility, data-driven decision-making, personalized interactions, real-time analytics, and streamlined communication within the distribution network. The incorporation of specific technologies and algorithms allows for efficient data management, secure communication, personalized experience, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain management.

[0091] Real-time Data Mesh 5 illustrates an RTDM module 500 according to one embodiment. The RTDM module 500 may be one embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0092] In some embodiments, the RTDM module 500 represents an effective data mesh and change capture component within an overall system architecture as depicted in Figure 5. The module is designed to provide real-time data management and reconciliation capabilities to enable efficient operations within the supply chain management domain.

[0093] The RTDM module 500 can include an integration layer 510 (also called "systems of record") that integrates with various enterprise systems. These enterprise systems can include ERPs, such as SAP®, Impulse, META, and I-SCALA, among others, as well as other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most current and accurate data.

[0094] The RTDM module 500 can include a data layer 520 configured to process and translate data for search and analysis. The RTDM module 500 creates a data mesh as a cloud-based infrastructure designed to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built data stores (PDSs) are deployed to store specific types of data, e.g., customer data, product data, or inventory data. Each PDS is optimized for efficient data retrieval based on specific use cases and requirements. A PDS is configured to store a specific type of data, e.g., customer data, product data, financial data, etc. These PDSs act as repositories of harmonized and standardized data, ensuring data consistency and integrity across systems.

[0095] In some embodiments, the RTDM module 500 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems like ERP (e.g., SAP®, Impulse, META, I-SCALA). The captured data is then processed and harmonized on the fly, and converted into a standardized format suitable for analysis and integration. This process ensures that data is readily available and up-to-date within the data mesh, facilitating real-time insight and decision making.

[0096] More specifically, data layer 520 within RTDM module 500 can be configured as a powerful and flexible foundation for managing and processing data within a supply chain ecosystem. In some embodiments, data layer 520 can encompass a highly scalable and robust data lake, which may be referred to as data lake 522, along with a set of purpose-built data stores (PDS), which may be referred to as PDS 524.1 through 524.N. These components work in unison to ensure efficient data management, reconciliation, and real-time availability.

[0097] In some embodiments, the data layer 520 includes a data lake 522, a novel storage and processing infrastructure designed to accommodate the ever-increasing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system, such as Apache® Hadoop® Distributed File System (HDFS) or Amazon® S3, the data lake can provide a unified, scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault-tolerance of cloud-based storage, the data lake 522 can aggregate and moderate the influx of data from diverse sources.

[0098] In association with the data lake 522, multiple purpose-specific data stores PDS 524.1 through 524.N may be employed. Each PDS 524 may act as a dedicated repository optimized for storing and retrieving a particular type of data related to the supply chain domain. In some non-limiting examples, PDS 524.1 may be dedicated to customer data storing information such as customer profiles, preferences, transaction history, etc. PDS 524.2 may focus on product data encompassing details regarding SKU codes, descriptions, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing to serve the diverse needs of supply chain participants.

[0099] To ensure real-time data synchronization, the data layer 520 can be configured to employ one or more advanced change data capture (CDC) mechanisms. These CDC mechanisms integrate with transactional systems such as legacy ERPs such as SAP®, Impulse, META, I-SCALA, and other enterprise-wide systems. The CDC constantly monitors and captures any updates, changes, or new transactions in these systems in real time. By capturing these changes, the data layer 520 ensures that the data within the data lake 522 and PDS 524 remains up-to-date, providing stakeholders with a real-time perspective on the supply chain ecosystem.

[0100] In some embodiments, data layer 520 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring compatibility with a wide range of existing systems and providing flexibility for customization and extensibility. For example, data layer 520 can utilize a Java technology stack, including frameworks such as Spring and Hibernate®, to facilitate integration with systems of record with a diverse population of ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0101] In some embodiments, to facilitate data processing and analysis, data layer 520 may include one or more distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks may enable parallel processing and distributed computing across large data sets stored in the data lake and PDS. By leveraging these frameworks, supply chain actors may perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, data layer 520 may leverage Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0102] In some embodiments, the data layer 520 can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify data within the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. Additionally, the data layer 520 can implement data lineage and audit trail mechanisms to enable stakeholders to track data provenance and history, ensuring data integrity and compliance with regulatory requirements.

[0103] In some embodiments, the data layer 520 can be deployed in a cloud-native environment, leveraging containerization technologies such as Docker® and orchestration frameworks such as Kubernetes®. This approach ensures scalability, resilience, and efficient resource allocation. For example, the data layer 520 can be deployed on cloud infrastructure provided by AWS®, Azure®, or Google® Cloud and leverage their managed services and scalable storage options. This allows for efficient scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0104] The Data Layer 520 of the RTDM module 500 can incorporate a highly scalable data lake, Data Lake 522, along with purpose-built PDSs, PDS 524.1 to 524.N, and by employing high-performance CDC mechanisms, the Data Layer 520 ensures efficient data management, reconciliation, and real-time availability. The integration of diverse technology stacks, e.g., .NET or Java, with distributed computing frameworks such as Apache Spark, enables powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, the Data Layer 520 ensures data integrity, confidentiality, and compliance. With its scalable infrastructure and efficient integration with existing systems, the Data Layer 520 empowers supply chain actors to make data-driven decisions to optimize operations and drive business success in dynamic and complex supply chain environments.

[0105] The RTDM module 500 can include an AI module 530 configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 520 to derive meaningful insights. In some non-limiting examples, the AI ​​module 530 can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 530 can continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI ​​module 530 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0106] The data engine layer 540 can comprise a set of interconnected systems responsible for the ingestion, processing, transformation, and integration of specialized data. Within the RTDM module 500, these systems include a collection of autonomously operating headless engines 540 that represent distinct functionality. These engines represent distinct functionality within the system and can include, for example, one or more recommendation engines, forecasting engines, and subscription management engines. Non-limiting examples of these headless engines include engines for subscriptions, solutions / bundles, ITAD (IT asset disposal), renewals, marketing, special pricing, financing, returns / claims, end users, order tracking, supermarket chains, search, vendor management, professional services, and ESG (environmental, social, and governance). These headless engines leverage harmonized data stored in the data mesh to provide specific business logic and services. The engines 540 can leverage harmonized data stored in the data mesh to provide specific business logic and services. Each engine is designed to be pluggable, allowing flexibility and future expansion of the module's functionality.

[0107] These systems can be configured to receive data from multiple sources, e.g., transactional systems, IoT devices, external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0108] Additionally, a data distribution mechanism 545 can be employed to facilitate integration and access into the RTDM module 500. The data distribution mechanism 545 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints including user interfaces, micro-frontends, and external systems.

[0109] The experience layer 550 focuses on providing an intuitive and user-friendly interface for interacting with supply chain data. The experience layer 550 can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0110] Thus, in some embodiments, the RTDM module 500 for supply chain management may include integration with systems of record and may include one or more data layers with data mesh and purpose-built data stores, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and efficient integration with existing enterprise systems. Technical feeds and searches within the module ensure that users can search relevant and up-to-date information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 500 facilitates supply chain management by providing a scalable, real-time data management solution. Its innovative architecture allows for rich integration of disparate data sources, efficient data reconciliation, and advanced analytical capabilities. The module provides distinct advantages of replicating and harmonizing data from diverse ERPs while maintaining the ability to maintain auditable and repeatable transactions, enabling a unified view for vendors, resellers, customers, end customers, and other entities in the distribution system, including IT distribution systems.

[0111] Single Pane of Glass UI 6 illustrates an SPoG UI according to one embodiment, generally designated as SPoG UI 600. In some embodiments, SPoG UI 600 can be an embodiment of SPoG UI 305 and represents a comprehensive, intuitive user interface designed to provide stakeholders with a unified, customizable view of the entire supply chain ecosystem. It combines a variety of features and functionality to enable users to gain a comprehensive understanding of their supply chains and efficiently manage their operations.

[0112] SPoG600 is capable of incorporating high velocity data in data-rich environments. In today's data-rich environments, traditional UI designs often struggle to present large amounts of information in a way that is understandable, efficient, and visually appealing. This challenge is amplified when data is dynamic, changes in real-time, and must be effectively displayed in a single-pane environment that prioritizes clean, whitespace-driven design.

[0113] The SPoG UI 600 can integrate functionality with the RTDM module 310 / 500 to provide stakeholders with a powerful and user-friendly interface for supply chain management. In some embodiments, the SPoG UI 600 can include a unified view (UV) module 605 to provide a customizable and holistic view of the supply chain, and a real-time data exchange module 610 to ensure accurate and up-to-date data synchronization based on the RTDM module 310 / 500. The collaborative decision-making module 615 fosters effective communication and collaboration among a diverse group of populations. The RBAC module 620 can be configured to secure access control. The customization module 625, the data visualization module 630, and the mobile and cross-platform accessibility module 635 can be configured to enhance user experience, data analysis, and accessibility, respectively. In some embodiments, the above-mentioned modules can enable stakeholders to make informed decisions, optimize supply chain operations, and promote business efficiency within the supply chain ecosystem.

[0114] The SPoG UI 600 can include a UV module 605, which provides stakeholders with a centralized, customizable dashboard-style layout. This module allows users to access real-time data, analytics, and functionality tailored to their specific roles and responsibilities within the supply chain ecosystem. The UV module 605 acts as a single entry point for users, providing a holistic and comprehensive view of supply chain operations and empowering them to make data-driven decisions. The UV module 605 can be configured to efficiently manage real-time data and maintain a visually clean interface without compromising performance. This innovative approach includes a unique composition of UI structure, responsive data visualization, real-time data handling methods, adaptive information architecture, and white space optimization.

[0115] UV Module 605 is built around a grid-based layout system and can leverage CSS Grid and Flexbox technologies. This structure provides the flexibility to create fluid layouts with elements that automatically adapt to the available space and content. HTML5 and CSS3 serve as the underlying technologies for creating the UI, while JavaScript, specifically React.js, manages the dynamic aspects of the UI.

[0116] The SPoG UI 600 integrates the UV module 605 with a real-time data exchange module 610 to facilitate the continuous exchange of data between the SPoG UI 600 and the RTDM module 310, leveraging one or more data sources, which may include one or more ERP, CRM, or other sources. Through this module, stakeholders can access up-to-date, accurate, and harmonized data. The real-time data synchronization ensures that the information presented in the SPoG UI 600 reflects the latest outlook and developments across the supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0117] In some embodiments, the collaborative decision-making module 615 in the SPoG UI 600 fosters real-time collaboration and communication between participants. This module allows for the exchange of information, initiation of workflows, and sharing of insights and recommendations. By integrating with the RTDM module 310 / 500, the collaborative decision-making module 615 ensures that participants can effectively collaborate based on accurate and synchronized data. This fosters overall operational efficiency and collaboration within the supply chain ecosystem.

[0118] To ensure secure and controlled access to functionality and data, the SPoG UI 600 incorporates a Role-Based Access Control (RBAC) module 620. Administrators can define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC module 620 can ensure that only authenticated users have access to certain features and information, protecting data privacy, security, and compliance within the supply chain ecosystem.

[0119] In some embodiments, the customization module 625 empowers users to personalize their dashboards and tailor the interface to their preferences and needs. Users can arrange widgets, charts, and data visualizations to prioritize the information most relevant to their specific roles and tasks. This module allows stakeholders to customize their view of their supply chain operations, providing a user-centric experience that improves productivity and ease of use.

[0120] The SPoG UI 600 can include a data visualization module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Leveraging advanced visualization techniques, this module presents complex data in a clear and intuitive manner. Users can gain insight into key performance indicators (KPIs), trends, patterns, and anomalies, facilitating data-driven decision-making and strategic planning.

[0121] The SPoG UI 600 can include a mobile and cross-platform accessibility module 635 to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the go. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring unified access to real-time data and functionality across a variety of devices.

[0122] It should be understood that the operations shown in the exemplary methods are not exhaustive and that other operations may similarly be performed before, after, or between any of the illustrated operations. In some embodiments of the present disclosure, operations may be performed in different orders and / or may be varied.

[0123] 7 is a flow diagram of a method 700 for performing comprehensive supply chain management operations using an SPoG UI, according to some embodiments of the present disclosure. In some embodiments, the method 700 provides operational steps for streamlining supply chain processes, improving decision making, and optimizing operations within a supply chain ecosystem. In some embodiments, the method 700 performs real-time data search, visualization, customization, collaboration, access control, and cross-platform accessibility functions through the SPoG UI. Based on the disclosure herein, the operations of the method 700 may be performed in different orders and / or with variations to suit particular implementation requirements.

[0124] In operation 705, the computing device receives user input through the SPoG UI representing a wide range of requests and commands related to supply chain management. User input encompasses actions such as selecting a particular data visualization, accessing a different module or function, initiating a workflow, configuring an interface, performing data-driven analysis, etc. This interactive input mechanism enables stakeholders to effectively engage with the SPoG UI and gain relevant insights to support the decision-making process.

[0125] In operation 710, the computing device processes the user input and leverages integration capabilities with the RTDM module to interact with the real-time data exchange module. This integration ensures efficient data retrieval and synchronization, allowing the computing device to access up-to-date and accurate information from diverse data sources within the supply chain ecosystem. By establishing a connection with the RTDM module and leveraging real-time data exchange, the computing device ensures that the perspectives presented in the SPoG UI reflect the latest developments and provide a comprehensive view of supply chain operations.

[0126] At operation 715, the computing device uses a data visualization module to generate visually appealing, interactive representations of the retrieved supply chain data. This module utilizes advanced visualization techniques to create dynamic dashboards, charts, graphs, and other visual elements to effectively communicate key performance indicators, trends, patterns, anomalies, and correlations within the supply chain ecosystem. Through these visualizations, stakeholders can gain valuable insights, identify critical areas, and assess the overall health of supply chain operations.

[0127] At operation 720, the computing device allows the user to personalize the dashboard and tailor the SPoG UI interface to their particular preferences and needs. The customization module empowers stakeholders to arrange widgets, charts, data visualizations, and other UI components to prioritize information most relevant to their roles and responsibilities. This flexibility ensures a user-centric experience, allowing stakeholders to focus on important data points and streamline decision-making processes within the SPoG UI.

[0128] In operation 725, the computing device facilitates real-time collaboration and communication between the participants through a collaborative decision-making module, which provides features that enable participants to exchange information, share insights and recommendations, initiate workflows, and participate in discussions within the SPoG UI interface. By integrating with the RTDM module, the collaborative decision-making module ensures that participants can effectively collaborate based on accurate and synchronized data, fostering a cohesive and agile supply chain ecosystem.

[0129] In operation 730, the computing device implements a secure access control mechanism through a role-based access control (RBAC) module integrated into the SPoG UI. This module allows administrators to define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. By implementing RBAC, the computing device protects data privacy, ensures confidentiality, and maintains regulatory compliance within the supply chain ecosystem. Authorized parties can access specific features, functionality, and information based on the roles granted to them, minimizing the risk of unauthorized access to or misuse of data.

[0130] In operation 735, the computing device optimizes the SPoG UI for integrated accessibility across multiple devices and platforms through a mobile and cross-platform accessibility module. This module ensures that stakeholders can access the SPoG UI interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected, informed, and engaged with supply chain operations while on the go. The interface is optimized to provide a consistent and intuitive user experience across different screen sizes, resolutions, and operating systems, facilitating real-time data access and improving stakeholder productivity.

[0131] In act 740, the computing device leverages a fast data module in a data-rich environment to efficiently deal with real-time data and maintain a visually clear interface. The module incorporates the unique composition of the SPoG UI structure, responsive data visualization, real-time data handling methods, adaptive information architecture, and optimization techniques. Act 740 can include processing large volumes of dynamic supply chain data in an understandable, efficient, and visually appealing manner. The SPoG UI's grid-based layout system is provided by CSS® Grid and Flexbox® technologies, allowing UI elements to fluidly adapt to available space and content while allowing HTML5, CSS3, and JavaScript (particularly React.js) to manage the dynamic aspects of the interface.

[0132] In summary, the method 700 depicted in FIG. 7 outlines a comprehensive approach to supply chain management through the SPoG UI. By leveraging real-time data search, visualization, customization, collaboration, access control, and cross-platform accessibility capabilities, stakeholders gain valuable insight into supply chain operations and can make informed decisions. The method facilitates efficient integration with RTDM modules to ensure accurate and up-to-date data synchronization. Through personalized dashboards, interactive data visualization, collaborative decision-making, secure access control, and cross-device accessibility, the SPoG UI empowers stakeholders to optimize supply chain operations, improve collaboration, and drive efficiency in a dynamic and complex supply chain ecosystem.

[0133] 8 is a flow diagram of a method 800 for vendor onboarding using the SPoG UI, according to some embodiments of the present disclosure. In some embodiments, the method 800 outlines a streamlined and efficient process that leverages the capabilities of the SPoG UI to facilitate vendor onboarding into a supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control functionality, the SPoG UI enables stakeholders to effectively manage and optimize the vendor onboarding process. Based on the disclosure herein, the operations of the method 800 may be performed in different orders and / or varied to suit particular implementation requirements.

[0134] In operation 805, the process begins when a vendor indicates an interest in joining the supply chain ecosystem. The computing device utilizes the SPoG UI to receive the vendor's information and related details, which may include company profile, contact information, product catalog, certifications, and any other relevant data required for the vendor onboarding process.

[0135] At operation 810, the computing device uses its integration capabilities with the real-time data exchange module to verify the vendor's information. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the vendor details are accurate and up-to-date. This verification step helps maintain data integrity, minimize errors, and establish a trusted foundation for the vendor onboarding process.

[0136] In operation 815, the computing device initiates a vendor onboarding workflow through a collaborative decision-making module. This module enables stakeholders involved in the onboarding process, such as procurement personnel, legal teams, and vendor managers, to collaborate and make informed decisions based on vendor information. The SPoG UI facilitates efficient communication, file sharing, and workflow initiation, enabling stakeholders to collectively evaluate a vendor's suitability and efficiently progress through the onboarding steps.

[0137] At operation 820, the computing device employs a role-based access control (RBAC) module to manage access control and permissions throughout the vendor onboarding process. The RBAC module ensures that participants have access only to the specific information and functionality required for their role. This control mechanism protects sensitive data, maintains privacy, and meets regulatory requirements. Authorized participants can securely review and contribute to the vendor onboarding process, fostering a transparent and compliant environment.

[0138] At act 825, the computing device provides stakeholders with a comprehensive view of the vendor onboarding process through a unified view (UV) module of the SPoG UI. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks pertaining to the vendor onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0139] At operation 830, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI, which provides dynamic visualization and analytics related to the vendor onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insight into the overall efficiency of the vendor onboarding process. This data-driven approach empowers stakeholders to make informed decisions, effectively allocate resources, and optimize onboarding workflows.

[0140] In act 835, the computing device facilitates integrated collaboration among the parties involved in the vendor onboarding process through a collaborative decision-making module. This module enables real-time communication, document sharing, and workflow coordination, allowing the parties to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approvals, the SPoG UI fosters efficient collaboration and reduces delays in the vendor onboarding workflow.

[0141] At operation 840, the computing device uses the workflow management module of the SPoG UI to ensure effective management and tracking of the vendor onboarding process. This module allows participants to define and manage the sequence of tasks, approvals, and reviews required for successful vendor onboarding. Workflow templates can be configured, allowing for standardization and repeatability of the onboarding process. Participants can monitor the status of each task, track completion, and receive notifications to ensure timely progress.

[0142] At act 845, the computing device captures and records vendor onboarding activities within the audit trail module of the SPoG UI. This module maintains a detailed history of the onboarding process, including actions taken, documents reviewed, and decisions made. The audit trail increases transparency, accountability, and compliance, providing stakeholders with a reliable record for future reference and potential audits.

[0143] At operation 850, the computing device finishes the vendor onboarding process within the SPoG UI. Once all necessary steps, reviews, and approvals are complete, the vendor is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, allowing them to confirm completion of all requirements and initiate further actions such as signing contracts, listing products, and collaboration.

[0144] In conclusion, method 800 depicted in FIG. 8 outlines a streamlined and efficient vendor onboarding process using the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and workflow management functionality, the SPoG UI empowers stakeholders to successfully onboard vendors into the supply chain ecosystem. The process ensures data accuracy, fosters transparency, improves collaboration, and promotes informed decision-making throughout the vendor onboarding workflow. The SPoG UI's intuitive interface, combined with customizable features and notifications, streamlines the onboarding process, reduces manual effort, and optimizes vendor integration within a dynamic and complex supply chain environment.

[0145] 9 is a flow diagram of a method 900 for reseller onboarding using the SPoG UI, according to some embodiments of the present disclosure. The method 900 outlines a streamlined and efficient process that leverages the capabilities of the SPoG UI to facilitate onboarding of resellers into a supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control functionality, the SPoG UI enables stakeholders to effectively manage and optimize the reseller onboarding process. Based on the disclosure herein, the operations of the method 900 may be performed in different orders and / or varied to suit particular implementation requirements.

[0146] At operation 905, the process begins when a reseller indicates an interest in joining the supply chain ecosystem. The computing device utilizes the SPoG UI to receive the reseller's information and related details, including company profile, contact information, corporate credentials, reseller agreement, and any other relevant data required for the reseller onboarding process.

[0147] At operation 910, the computing device uses its integration with the real-time data exchange module to verify the reseller's information. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the reseller's details are accurate and up-to-date. This verification step helps maintain data integrity, minimizing errors and establishing a trusted foundation for the reseller onboarding process.

[0148] In operation 915, the computing device initiates a reseller onboarding workflow through a collaborative decision-making module. This module enables the parties involved in the onboarding process, e.g., sales representatives, legal teams, account managers, to collaborate and make informed decisions based on the reseller's information. The SPoG UI facilitates integrated communication, file sharing, and workflow initiation, enabling the parties to collectively assess the reseller's suitability and efficiently progress through the onboarding steps.

[0149] At operation 920, the computing device employs a role-based access control (RBAC) module to manage access control and permissions throughout the reseller onboarding process. The RBAC module ensures that participants have access only to the specific information and functionality required for their role. This control mechanism protects sensitive data, maintains privacy, and meets regulatory requirements. Authorized participants can securely review and contribute to the reseller onboarding process, fostering a transparent and compliant environment.

[0150] At act 925, the computing device provides stakeholders with a comprehensive view of the reseller onboarding process through a unified view (UV) module of the SPoG UI. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks pertaining to the reseller onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0151] At operation 930, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI, which provides dynamic visualization and analytics related to the reseller onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insight into the overall efficiency of the onboarding process. This data-driven approach empowers stakeholders to make informed decisions, effectively allocate resources, and optimize the reseller onboarding workflow.

[0152] In operation 935, the computing device facilitates efficient collaboration among the parties involved in the reseller onboarding process through a collaborative decision-making module. This module enables real-time communication, document sharing, and workflow coordination, allowing the parties to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approvals, the SPoG UI fosters efficient collaboration and reduces delays in the reseller onboarding workflow.

[0153] At operation 940, the computing device records and maintains an audit trail of the reseller onboarding activities within the audit trail module of the SPoG UI. This module captures detailed information about actions taken, decisions made, and documents reviewed during the onboarding process. The audit trail increases transparency, accountability, and compliance, and serves as a valuable reference for future audits, reviews, and assessments.

[0154] At act 945, the computing device finishes the reseller onboarding process within the SPoG UI. Once all required tasks, reviews, and approvals are completed, the reseller is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensures that all requirements have been met, and facilitates further actions such as signing contracts, listing products, and collaborating with the reseller.

[0155] In conclusion, method 900 depicted in FIG. 9 highlights a streamlined and efficient reseller onboarding process using SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and audit trail functionality, SPoG UI empowers stakeholders to successfully onboard resellers into the supply chain ecosystem. SPoG UI's intuitive interface, customizable features, and robust collaboration capabilities streamline the onboarding process, improve transparency, and foster efficient communication between stakeholders. SPoG UI's data visualization capabilities facilitate data-driven decision-making, while the audit trail ensures compliance and provides a reliable record of onboarding activities. Through effective utilization of SPoG UI, the reseller onboarding process becomes a well-orchestrated workflow, optimizing reseller integration and fostering business success in a dynamic supply chain environment.

[0156] FIG. 10 is a flow diagram of a method 1000 for customer and end-customer onboarding using the SPoG UI, according to some embodiments of the present disclosure. The method 1000 outlines a comprehensive, user-centric approach for efficiently onboarding customers and end-customers into a supply chain ecosystem. By leveraging the capabilities of the SPoG UI, including real-time data integration, collaborative decision-making, and personalized user experience, participants can successfully onboard and engage customers and provide an efficient and adapted onboarding experience. Based on the disclosure herein, the operations of the method 1000 can be performed in different orders and / or varied to suit specific implementation requirements.

[0157] In operation 1005, the process begins when a potential customer or end customer indicates an interest in joining the supply chain ecosystem. The computing device utilizes the SPoG UI to capture the customer's or end customer's information, preferences, and requirements necessary for the onboarding process, including contact details, company profile, industry specific preferences, and any other relevant data.

[0158] In operation 1010, the computing device validates the customer or end-customer information using real-time data integration capabilities with external systems. By synchronously accessing data from various sources, such as customer relationship management (CRM) systems, or other enterprise-wide solutions, the computing device ensures the accuracy and completeness of the customer or end-customer details. This validation step helps establish a trusted foundation for the onboarding process and improves data integrity.

[0159] In operation 1015, the computing device initiates a customer or end-customer onboarding workflow through a collaborative decision-making module. This module facilitates integrated communication and collaboration between the stakeholders involved in the onboarding process, such as sales representatives, account managers, and customer support teams. The SPoG UI provides a centralized platform for stakeholders to collectively assess customer requirements, define personalized onboarding journeys, and make informed decisions throughout the onboarding process.

[0160] At operation 1020, the computing device uses a role-based access control (RBAC) module to manage access control and permissions during the onboarding process. The RBAC module ensures that participants have appropriate access to customer or end-customer data based on their roles and responsibilities. This control mechanism protects sensitive data, maintains data privacy, and meets regulatory requirements. Authorized participants can securely review, update, and track the onboarding process, fostering a transparent and compliant onboarding environment.

[0161] At act 1025, the computing device leverages the unified view (UV) module of the SPoG UI to provide stakeholders with a comprehensive, customizable, dashboard-style layout of the customer or end-customer onboarding process. This module aggregates relevant information, tasks, and milestones pertaining to the onboarding journey, providing stakeholders with a holistic view of the onboarding process. Stakeholders can monitor status, review documentation, and access real-time updates to ensure an efficient and integrated onboarding experience.

[0162] At operation 1030, the computing device utilizes the data visualization module of the SPoG UI to provide dynamic visualization and analytics related to the onboarding process. Through interactive charts, graphs, and reports, stakeholders gain insight into key onboarding metrics, customer engagement levels, and potential bottlenecks. This data-driven approach empowers stakeholders to make informed decisions, optimize onboarding strategies, and personalize the onboarding experience for each customer or end customer.

[0163] In operation 1035, the computing device enables the participants to interact with the collaborative decision-making module to facilitate integrated collaboration during the onboarding process. Parties can share documents, initiate workflows, and exchange information in real-time. The SPoG UI fosters efficient communication, reduces latency, and ensures alignment between the participants involved in customer or end-customer onboarding.

[0164] At operation 1040, the computing device employs a customization module to enable participants to personalize the onboarding experience for each customer or end customer. Parties can tailor interfaces, workflows, and communications to align with the customer's or end customer's preferences, industry-specific requirements, and strategic goals. The customization feature increases customer satisfaction and engagement during the onboarding journey.

[0165] At operation 1045, the computing device utilizes an audit trail module within the SPoG UI to maintain a detailed record of customer or end customer onboarding activities. This module captures information regarding actions taken, decisions made, and documents reviewed throughout the onboarding process. The audit trail increases transparency, accountability, and compliance and serves as a valuable reference for future audits, reviews, and assessments.

[0166] At act 1050, the computing device finishes the customer or end customer onboarding process within the SPoG UI. Once all required tasks, reviews, and approvals are completed, the customer or end customer is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensures all requirements have been met, and facilitates further actions such as account activation, service delivery, personalized customer engagement, etc.

[0167] In conclusion, method 1000 depicted in FIG. 10 illustrates the customer and end-customer onboarding process facilitated by the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, customization, and audit trail functionality, the SPoG UI empowers stakeholders to successfully onboard customers and end-customers into the supply chain ecosystem. The SPoG UI's intuitive interface, personalized features, and robust collaboration capabilities streamline the onboarding process, improve transparency, and foster efficient communication between stakeholders. The SPoG UI's data visualization capabilities facilitate data-driven decision-making, while the customization and audit trail modules ensure a tailored and compliant onboarding experience. Through effective utilization of the SPoG UI, the customer and end-customer onboarding process becomes an integrated workflow, optimizing customer and end-customer integration and fostering business success within a dynamic supply chain environment.

[0168] FIG. 11 depicts a system 1100 for automated SKU management, according to some embodiments. In some embodiments, the system 1100 can leverage the functionality of the UI 1105, data layer 1110, and catalog translation module 1115 to provide a comprehensive solution for SKU management. The system 1100 can be configured to improve the accuracy and efficiency of the SKU management process by enabling data exchange, real-time SKU creation, and dynamic pricing. The system 1100 can further be configured to empower vendors and entities to effectively manage SKUs, optimize inventory levels, and provide a superior customer experience in a rapidly evolving marketplace.

[0169] A system 1100 is provided for efficiently managing a large number of SKUs in a distribution platform. The system aims to automate SKU management and integrate with vendors to facilitate easy self-service. In some embodiments, one or more SKUs can be generated as virtual SKUs until an order is placed, at which point they become real SKUs. The entire process is designed to be synchronous, ensuring an efficient and integrated experience for the customer. By presenting the SKUs in the customer platform, the system can address a huge number of SKUs without burdening the back-end systems. This approach allows for scalability and avoids operational overhead. The SKU creation process utilizes AI / ML algorithms and proprietary algorithms within the distribution platform to transform the catalog and create virtual SKUs. Data integrity and validation are key aspects addressed by the system, especially in the Master Data Governance (MDG) module. The MDG module ensures data accuracy, consistency, and quality control. The system also incorporates a pricing engine for fetching and computing prices for SKUs, including special pricing. The system's architecture allows for dynamic SKU creation, efficient data processing, and integration with vendors, thereby enabling scalability and aggregation of products from multiple vendors. The virtual SKU concept, powered by AI / ML algorithms and rules engine, is a significant paradigm shift that overcomes scaling challenges to address a wider range of SKUs. Through self-service capabilities, vendors can upload their catalogs without manual intervention, reducing operational overhead. The system incorporates caching mechanisms and data governance practices to manage SKU lifecycle, identify inactive SKUs, and retain historical data for compliance and security purposes. Overall, the system's innovative approach for SKU management, virtual SKU creation, and pricing optimization drives scalability, operational efficiency, and improved customer experience.

[0170] In some embodiments, the system 1100 can include one or more interconnected modules and subsystems, each performing a specific function and contributing to the management of SKUs. In some embodiments, these components can include a catalog transformation module, a real-time data mesh (RTDM) module, a master data governance (MDG) module, a global data repository (GDR), a search platform, a dynamic SKU creation module, and a global pricing engine (GPE). In some embodiments, the real-time data mesh (RTDM) module (reference number 1110) and the engine layer (reference number 1140) play an important role in facilitating real-time data management and processing.

[0171] In some embodiments, the UI 1105 can serve as a central point of interaction for a user within the system 1100. In a non-limiting example, the UI 1105 can be an embodiment of an SPoG UI, such as SPoG UI 305, 410, 600, or any other UI, that allows a user, e.g., one or more vendors, to easily navigate through different modules, access related information, and perform various tasks related to SKU management. The UI 1105 is designed to be intuitive, user-friendly, and responsive, allowing a user to efficiently interact with the system.

[0172] In some embodiments, the data layer 1110 can be configured to enable efficient data flow across the SKU management ecosystem. The data layer 1110 can be an embodiment of the RTDM module 310, 415, 500, or any other data layer, and can encompass a data lake that serves as a scalable and robust storage infrastructure for storing structured and unstructured data related to SKUs. In some embodiments, the data layer 1110 is integrated with the RTDM module to enable real-time data exchange and synchronization. This integration ensures that the data within the data layer 1110 is up-to-date and readily available for SKU management operations.

[0173] In some embodiments, the data layer 1110 can be an embodiment of a RTDM module, such as RTDM module 310 or RTDM module 415, or RTDM module 500. In some embodiments, the data layer 1110 can be a separate data layer that interacts with the RTDM module. As described above, the RTDM module can be configured to function as an ERP-agnostic real-time data mesh. In some embodiments, the RTDM module collects data from the system of record layer, including data from various enterprise systems such as ERP, and incorporates it into a data lake within the data layer 1110.

[0174] The Data Layer 1110 acts as a repository of harmonized and standardized data obtained from the RTDM modules. Various purpose-built data stores are deployed inside the Data Layer 1110 to store specific types of data, e.g., customer data, product data, financial data, etc. These purpose-built data stores optimize data retrieval based on specific use cases and requirements to ensure efficient SKU management.

[0175] In the dynamic SKU process, when a customer adds a non-transactional product to the cart, the data layer 1110 interacts with the RTDM module to facilitate real-time SKU creation. With access to the most up-to-date and accurate data, the data layer 1110 provides the information needed for the dynamic SKU creation module, which uses data from the data layer 1110 along with other relevant data sources to generate SKUs in real-time inside the ERP system.

[0176] By leveraging data available within the data layer 1110 and the real-time capabilities of the RTDM module, the system 1100 is configured to enable an efficient and accurate SKU creation process consistent with up-to-date information. This interaction between the data layer 1110 and the RTDM module facilitates an integrated data flow, enabling a dynamic SKU creation process to be effectively executed within the entire SKU management ecosystem.

[0177] In some embodiments, the catalog transformation 1115 can be an embodiment of an AAML module, such as AAML module 315 or AI module 420. In some embodiments, the catalog transformation 1115 can be a separate data layer that interacts with the AAML or AI modules. In a non-limiting example, the catalog transformation module 1115 can employ advanced AI / ML algorithms leveraging frameworks such as TensorFlow and PyTorch. These algorithms are trained on a vast dataset of existing vendor catalog files that are categorized based on a comprehensive data governance process. By utilizing deep learning techniques and neural networks, the module gains the ability to accurately suggest category classifications and attribute mappings for new catalogs with an accuracy of over 80%.

[0178] In some embodiments, the catalog transformation module 1115 operates by processing vendor catalog files received from the UI 1105 of the system 1100. It applies trained AI / ML models, which may include classification and clustering algorithms, to predict the most suitable category classifications and attribute mappings for each item within the catalog.

[0179] To achieve optimal results, the catalog transformation module 1115 can consider multiple data points including product descriptions, keywords, and historical mapping patterns. Natural Language Processing (NLP) techniques can be employed to parse and extract meaningful information from text data, enabling accurate category classification and attribute mapping.

[0180] In addition, the catalog translation module 1115 can be configured to promote flexibility and adaptability in the catalog mapping process. A feedback mechanism can be provided that allows vendor users or internal associates to review and refine the proposed category classifications and attribute mappings. This feedback can be incorporated into the iterative learning process of the module to further improve accuracy and performance over time.

[0181] In a non-limiting example, consider a vendor catalog containing electronic and / or IT products such as laptops, servers, and other computing devices. The catalog transformation 1115 module can analyze the product descriptions to identify relevant keywords and apply clustering algorithms to group similar products together. It can then assign appropriate categories and map relevant attributes such as brand, model, specifications, pricing, etc. to each item.

[0182] In some embodiments, the catalog transformation module 1115 collaborates with a master data governance (MDG) module 1120 to ensure data integrity and validation. The catalog transformation module 1115 can be configured to communicate with the MDG module to validate the transformed catalog and identify errors or inconsistencies in the categorization and attribute mapping process. Indications or notifications, such as flags, are generated to alert vendors, administrators, etc. of these issues and allow them to make necessary corrections and updates.

[0183] The catalog conversion module 1115 operates within the broader system 1100 architecture and can be configured to interact with other components, such as the Real Time Data Mesh (RTDM) module (reference number 1110). Through this interaction, the converted catalog is synchronized in real time to ensure the availability of accurate and up-to-date product information throughout the system.

[0184] The Catalog Transformation module 1115 leverages advanced AI / ML algorithms and a comprehensive technology stack to convert vendor catalog files into a standardized format. Through the application of deep learning techniques and an iterative learning process, it accurately predicts the category classification and attribute mapping for each catalog item. This module improves the efficiency of SKU management, streamlines operations, and provides businesses with the ability to effectively manage their product catalogs.

[0185] The AI / ML module (AAML) 1115 leverages advanced analytics and machine learning algorithms to enhance the SKU management capabilities within the system 1100. In some non-limiting examples, the AAML 1115 can utilize technologies such as Apache Spark, TensorFlow, and scikit-learn to extract valuable insights from data. These algorithms enable the AAML 1115 to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall SKU management efficiency.

[0186] In a non-limiting example, a catalog conversion module within the system 1100 utilizes the data layer 1110 and the AAML 1115 to convert diverse catalog files into a standardized format. Trained by existing catalog datasets, the AAML 1115 predicts category classifications and attribute mappings for new catalogs, ensuring accurate and efficient conversion. The converted catalogs are validated by the MDG module and stored in the GDR to ensure data integrity and consistency.

[0187] The RTDM module integrated with the data layer 1110 enables real-time data synchronization across the SKU management ecosystem. It facilitates data exchange and ensures that all system components have access to the most up-to-date information of SKU management operations. This real-time data exchange capability improves the accuracy and efficiency of the SKU management process.

[0188] The Dynamic SKU Creation module collaborates with the GDR and RTDM modules to enable real-time creation of SKUs for non-transactional products. When customers add these products to their cart, the Dynamic SKU Creation module generates SKUs in the ERP system to facilitate order processing and fulfillment. GPE integrates with the data layer 1110 to determine the pricing of catalog items in real-time based on vendor price files, market trends, and historical pricing data.

[0189] In some embodiments, the SPoG UI 1105 presents a unified view of various system elements and functionality, providing a user-friendly experience by aggregating multiple system components into a single platform. It supports multiple data inputs, including vendor catalogs in various formats. In some embodiments, the SPoG UI 1105 receives this catalog data and interfaces with other system components to enable efficient SKU management.

[0190] In some embodiments, the RTDM module 1110 is a key component that facilitates real-time data synchronization across system components. It works in conjunction with the SPoG UI 1105 to ensure that the interface displays accurate real-time data, which is important during SKU creation and inventory management. In some embodiments, the RTDM 1110 allows for real-time interaction between users and the system, allowing for immediate updates to be reflected across the system.

[0191] By incorporating the AAML module 1115, the system uses machine learning models to convert catalog files into a standardized format. The module predicts and suggests categorizations and attribute mappings for catalog items, maintaining a feedback loop to improve prediction accuracy over time. AAML 1115 is trained by existing datasets and uses iterative learning to improve its predictions, contributing to robustness and versatility in dealing with diverse catalog formats.

[0192] The MDG module 1120 validates the converted catalog to ensure accuracy and reliability of the data. In some embodiments, the MDG 1120 communicates errors back to the vendor, allowing them to correct and re-upload the corrected catalog data via the SPoG UI 1105. This continuous feedback loop further enriches the data input to the AAML module 1115, improving overall system performance and accuracy.

[0193] The GDR 1125 plays a key role in storing the validated catalog and maintaining data integrity. It collaborates with the RTDM 1110 to support real-time data synchronization, ensuring that all components have access to the most accurate and up-to-date data. The GDR 1125 also facilitates the MDG 1120 by storing records of previously validated catalogs, thereby providing a comprehensive repository for the system.

[0194] The Search Platform 1130 is integrated with searching and indexing of the stored catalogue and works synergistically with the SPoG UI 1105 to provide users with a product discovery experience. The Search Platform 1130 also interacts with the RTDM 1110 for real-time data updates to improve the relevance and accuracy of search results.

[0195] In some embodiments, the system 1100 may include additional modules, such as a dynamic SKU creation module 1135 and a global pricing engine (GPE) 1140. The dynamic SKU creation module 1135 dynamically generates SKUs for non-transactional products during checkout and allows these products to be added to a customer's cart, while the GPE 1140 determines real-time pricing for catalog items based on a variety of factors, such as vendor price files, market trends, historical pricing data, etc.

[0196] As described above, the data layer 1110 can be an embodiment of a RTDM module (e.g., 310, 415, 500) that leverages data mesh principles to integrate interconnected components, processes, and subsystems to enable efficient real-time data management and analysis. In one non-limiting example, the RTDM module 1110 can incorporate a cloud-based infrastructure that includes a data lake (e.g., data lake 522 depicted in FIG. 5) and purpose-specific data stores (PDS) (e.g., PDS 524.1 through 524.N). The data lake can be configured to provide scalable and fault-tolerant data storage capabilities, while the PDS is a repository optimized to store specific types of data related to the SKU management domain.

[0197] Engine layer 1140 in system 1100 represents a collection of interconnected systems responsible for the ingestion, processing, transformation, and integration of specialized data. Engine layer 1140 can be an embodiment of one or more of engines 540, as depicted in FIG. 5. For example, engine layer 1140 can include one or more engines configured to operate autonomously within the system and perform a particular functionality, such as an engine configured to determine real-time pricing for one or more catalog items based on a variety of factors, such as vendor price files, market trends, and historical pricing data.

[0198] In one non-limiting example, the engine layer 1140 can include recommendation engines, outlook engines, subscription management engines, and various other specialized engines tailored to meet the specific needs of SKU management. These engines leverage the harmonized data stored in the data mesh to deliver targeted business logic and services.

[0199] The engine layer 1140 is designed to receive data from multiple sources, including transactional systems, IoT devices, and external data providers. It processes the incoming data, applying algorithms to cleanse, aggregate, and enrich the data, and prepares it for further analysis and integration within the system.

[0200] In some embodiments, the engine layer 1140 further incorporates a data distribution mechanism (reference number 1145) similar to the data distribution mechanism 545 of Figure 5. This mechanism includes one or more APIs that facilitate the distribution of data from the data mesh and engine to various endpoints, including user interfaces and external systems.

[0201] The engine layer 1140 is an embodiment of the headless engine 540 and performs operations to facilitate the SKU management system and can contribute functionality and advanced algorithms for category assignment, pricing, and other tasks related to SKU management. The engine layer 1140 can be operatively connected to other engines and interconnected elements of the RTDM module 500, which operate autonomously to provide specialized services and enable efficient processing of incoming catalog items.

[0202] For example, the engine layer 1140 may include a category assignment engine (CAE) responsible for assigning categories to catalog items based on attributes and characteristics. In some embodiments, the CAE may be configured to perform one or more techniques, including natural language processing (NLP), machine learning, and rule-based systems, to provide accurate, automated category classification.

[0203] Using NLP algorithms, CAE parses the textual descriptions of catalog items and extracts relevant keywords and phrases. It can then map the terms to a predefined category hierarchy, taking into account synonyms, abbreviations, and variations in language usage. Machine learning models trained on vast amounts of historical data recognize patterns and relationships between attributes and categories, helping to increase the accuracy of category classification.

[0204] In addition, CAE utilizes a rules-based system to incorporate specific business rules and logic. These rules can be customized to the unique requirements of the SKU management system, allowing fine-grained control over category assignment. For example, rules may be defined to prioritize certain attributes or consider certain criteria when assigning categories to ensure consistent and accurate results.

[0205] To support the pricing aspect of the SKU management system, the engine layer 1140 incorporates a pricing engine (PE), which determines the optimal pricing for catalog items based on a variety of factors. The PE considers inputs such as vendor price files, market trends, historical pricing data, predefined pricing algorithms or rules, etc.

[0206] PE leverages advanced algorithms to analyze pricing inputs and generate prices that are both competitive and profitable. For example, machine learning algorithms such as regression models or neural networks may be employed to predict optimal price ranges based on historical sales data and market dynamics. Alternatively, a rules-based system may be utilized that incorporates pricing strategies and guidelines set by the business.

[0207] The engine layer 1140 also interfaces with the Real-Time Data Mesh (RTDM) module, a scalable, fault-tolerant data storage infrastructure that ensures real-time data management and analysis. The RTDM module integrates with various enterprise systems, such as ERP, to capture real-time changes and harmonize data for efficient processing inside the SKU management system.

[0208] By leveraging harmonized and standardized data within the RTDM module, engines in the engine layer 1140 make informed decisions during the category allocation and pricing process. They retrieve relevant information from the RTDM module, such as customer data, product data, financial data, etc., to improve accuracy and consistency.

[0209] Additionally, the engine layer 1140 incorporates advanced data processing capabilities through technologies such as Apache Spark or Apache Flink. These distributed computing frameworks enable parallel processing and distributed computing across large data sets, facilitating efficient analytics and computations for category assignment and pricing.

[0210] In some embodiments, the engine layer 1140 may also utilize machine learning algorithms, such as clustering or classification models, to identify patterns or group similar catalog items. This can aid in accurate category assignments and facilitate dynamic pricing strategies based on product similarities or customer segmentation.

[0211] Additionally, the engine layer 1140 encompasses fine-grained access control mechanisms and authentication protocols to ensure data security and prevent unauthorized access to sensitive information. Data lineage and audit trail mechanisms track data provenance and history, ensuring compliance with regulatory requirements and maintaining data integrity.

[0212] The engine layer 1140 operates in real-time, continuously monitoring incoming catalog items and updating category assignments and pricing information as new data becomes available, efficiently dealing with large volumes of catalog items and ensuring scalability and performance within the SKU management system.

[0213] The engine layer 1140 is integrated with the RTDM module to utilize advanced algorithms and incorporate customizable business rules to ensure accurate category classification and competitive pricing, ultimately driving productivity and customer satisfaction. By leveraging the capabilities of the engine layer 1140, the system 1100 enables vendors and other entities to automate and streamline category assignment and pricing operations, reducing manual efforts and improving overall efficiency. Through integration with the RTDM module, advanced algorithms, and data processing techniques, the headless engine of the engine layer 1140 is empowered to deliver accurate and timely results, improving the effectiveness of the SKU management system inside the system 1100.

[0214] The combination of RTDM module 1110, engine layer 1140, and other system components within the system 1100 enables data flow, real-time insight, and efficient management of SKU-related information. Through the integration of diverse technology stacks, data processing frameworks, and distribution mechanisms, businesses can leverage the power of the system 1100 to streamline SKU management processes, optimize operations, and make data-driven decisions.

[0215] Overall, the RTDM module 1110 and engine layer 1140 within the system 1100 provide the necessary infrastructure and capabilities to address real-time data management, processing, and analysis to ensure efficient SKU management operations.

[0216] FIG. 11 depicts one embodiment of a SKU management system referred to as system 1100. In this implementation, system 1100 incorporates certain components that enable efficient and accurate SKU management, including a user interface (UI) 1105, a data layer 1110, and an AI / ML module (AAML) 1115. These components work together to streamline the process of creating, managing, and pricing SKUs, improving the efficiency of the overall system. FIG. 11 depicts one embodiment of a SKU management system referred to as system 1100. In this implementation, system 1100 incorporates certain components that enable efficient and accurate SKU management, including a user interface (UI) 1105, a data layer 1110, and an AI / ML module (AAML) 1115. These components work together to streamline the process of creating, managing, and pricing SKUs, improving the efficiency of the overall system.

[0217] Figure 12 depicts a process flow for SKU generation that is an integral part of the onboarding process within system 1200 as described in Figure 11. This process flow outlines the steps involved in efficiently generating SKUs based on vendor catalog files and ensuring accuracy, real-time synchronization, and integration within the SKU management ecosystem.

[0218] Operation 1205 includes initiating a SKU generation process when the system receives vendor catalog files. These catalog files contain information about products offered by vendors, including descriptions, attributes, pricing details, etc. Triggering the SKU generation process enables the system to convert these catalogs into standardized structured data, facilitating efficient SKU management.

[0219] Operation 1210 may include a catalog transformation step, within which advanced AI / ML algorithms are applied to vendor catalog files to convert them into a standardized format. Leveraging techniques such as deep learning and neural networks, the algorithms predict classification and attribute mappings for each item in the catalog. These predictions are based on analysis of product descriptions, keywords, past mapping patterns, and other relevant data points. The catalog transformation module further improves the accuracy of the predictions by using natural language processing (NLP) techniques to extract meaningful information from text data. The module also enables feedback and refinement of the proposed category classifications and attribute mappings, incorporating an iterative learning process to continually improve the accuracy of the predictions.

[0220] To ensure real-time availability and synchronization of SKU information, operation 1215 may include real-time data synchronization. The converted catalog from the catalog conversion step is synchronized with a data layer internal to the system. This synchronization ensures that SKU information is kept up to date and easily available for SKU management operations. By integrating a real-time data mesh (RTDM) module, the system facilitates data exchange and synchronization, allowing stakeholders to access the most accurate and up-to-date SKU information.

[0221]

[0234] Operation 1220 may include a Master Data Governance (MDG) step, which is important for maintaining data integrity and validation. The converted catalog is validated within the MDG module to ensure its accuracy, consistency, and compliance with data governance practices. This validation process identifies errors or inconsistencies in the catalog's categorization and attribute mappings. Stakeholders, such as vendors or administrators, are notified of these issues through instructions or notifications, allowing them to make necessary corrections and updates. The MDG module plays a key role in maintaining data quality and reliability throughout the SKU generation process.

[0222]

[0235] Operation 1225 may include data storage and management. The validated and transformed catalog is stored in a Global Data Repository (GDR) to ensure data integrity and accessibility. The GDR serves as a central repository for standardized and harmonized data derived from the Real-Time Data Mesh (RTDM) module. This data storage mechanism supports real-time data synchronization and facilitates the availability of accurate and up-to-date SKU information across systems. Additionally, the GDR retains historical data for compliance and security purposes and provides a comprehensive repository of SKU-related information.

[0223]

[0236] Operation 1230 may include a dynamic SKU creation step, during which the system generates SKUs for non-transactional products in real-time, typically during the checkout process. By leveraging the transformed and synchronized catalog data, the system dynamically creates SKUs within the ERP system. This enables efficient order processing and fulfillment by associating appropriate SKUs with selected products. The real-time nature of this process ensures that customers can add non-transactional products to their cart and complete their purchase without delay.

[0224]

[0237] Operation 1235 may include determining pricing. Within this step, the system determines real-time pricing for the catalog items. The pricing engine integrates with the data layer to fetch relevant data such as vendor price files, market trends, and historical pricing data. Using advanced algorithms such as machine learning models or rule-based systems, the pricing engine analyzes this data and generates competitive and profitable prices for the catalog items. Real-time pricing determination improves the overall pricing optimization process and ensures accurate and up-to-date pricing information for SKU management operations.

[0225] Operation 1240 may include the termination and validation of the SKU generation process. At this stage, the entire SKU generation process is completed and the system validates the generated SKUs and pricing information for accuracy. Any necessary actions, such as order processing or further SKU management operations, can be initiated based on the generated SKUs. This step brings to a close the SKU generation process designed to improve the efficiency, accuracy, and integration of SKU management within the system.

[0226] The process flow depicted in FIG. 12 illustrates the steps involved in the SKU generation process associated with the onboarding process within system 1200. By leveraging advanced AI / ML algorithms, real-time data synchronization, and data governance practices, the system ensures accurate and timely generation of SKUs based on vendor catalog files. This process flow supports consolidation, scalability, and efficiency in managing a large number of SKUs, ultimately improving the overall SKU management capabilities within the system.

[0227] 13 is a block diagram of example components of a device 1300. One or more computer systems 1300 may be used to implement, for example, any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1300 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1304. The processor 1304 may be connected to a communications infrastructure, i.e., bus 1306.

[0228] The computer system 1300 may also include user input / output devices 1303 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1306 through a user input / output interface 1302 .

[0229] One or more of the processors 1304 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that is a specialized electronic circuit designed to handle mathematically intensive applications. A GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, videos, etc.

[0230] The computer system 1300 may also include a main or primary memory 1308, such as a random access memory (RAM). The main memory 1308 may include one or more levels of cache. The main memory 1308 may have control logic (i.e., computer software) and / or data stored therein.

[0231] The computer system 1300 may also include one or more secondary storage devices or memory 1310. The secondary memory 1310 may include, for example, a hard disk drive 1312 and / or a removable storage device or drive 1314.

[0232] The removable storage drive 1314 may interface with a removable storage unit 1318. The removable storage unit 1318 may include a computer usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1318 may be a program cartridge and cartridge interface (e.g., as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 1314 may read from and / or write to the removable storage unit 1318.

[0233] The secondary memory 1310 may include other means, devices, components, intermediaries, or other approaches for allowing computer programs and / or other instructions and / or data to be accessed by the computer system 1300. Such means, devices, components, intermediaries, or other approaches may include, for example, a removable storage unit 1322 and an interface 1320. Examples of removable storage units 1322 and interfaces 1320 may include a program cartridge and a cartridge interface (e.g., as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and a USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0234] Computer system 1300 may further include a communication or network interface 1324. Communication interface 1324 may enable computer system 1300 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference number 1328). For example, communication interface 1324 may enable computer system 1300 to communicate with external or remote devices 1328 via communication paths 1326, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from computer system 1300 via communication paths 1326.

[0235] Additionally, computer system 1300 may be a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smart watch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0236] The computer system 1300 may be a client or server accessing or hosting any application and / or data through any delivery paradigm, including, but not limited to, remote or distributed cloud computing solutions, local or on-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.), and / or hybrid models including any combination of the foregoing examples or other service or delivery paradigms.

[0237] Any applicable data structures, file formats, and schemas in computer system 1300 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used, either exclusively or in combination with known or open standards.

[0238] In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1300, main memory 1308, secondary memory 1310, and removable storage units 1318 and 1322, as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1300), may cause such data processing devices to operate as described herein.

[0239] Figures 14A through 14Q depict various screens and functionality of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription changes. A detailed description of each drawing is provided below.

[0240] FIG. 14A depicts the Vendor Onboarding Start Screen, which represents the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in joining the supply chain ecosystem. Vendors can enter basic information such as company details, contact information, product catalog, etc.

[0241] FIG. 14B depicts a vendor onboarding guide that displays a step-by-step guide or checklist that a vendor follows during the onboarding process. It outlines the tasks and requirements necessary to ensure that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0242] FIG. 14C depicts a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform associates or representatives who will guide them through the onboarding process. Vendors can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0243] FIG. 14D depicts a Vendor Onboarding Task List that presents a comprehensive task list or dashboard that outlines the specific steps and actions required for successful vendor onboarding. An overview of pending tasks, completed tasks, and upcoming deadlines is provided to help vendors track progress and ensure timely completion of each onboarding task.

[0244] 14E depicts a vendor onboarding completion screen confirming successful completion of the vendor onboarding process, which may display a congratulatory message or a summary of completed tasks indicating that the vendor is now officially onboarded into the supply chain ecosystem.

[0245] FIG. 14F depicts a Partner Dashboard that provides partners or stakeholders with a centralized view of relevant information and metrics related to their partnership with the supply chain ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision making.

[0246] FIG. 14G depicts a Customer Product Cart, which represents a customer's product cart, to which the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding with the checkout process.

[0247] FIG. 14H depicts a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and change subscription details before finalizing their selection.

[0248] FIG. 14I depicts a Customer Order Summary that provides a summary of a customer's order, including details such as products or subscriptions purchased, quantities, pricing, and any discounts or promotions applied, allowing the customer to review their order before confirming the purchase.

[0249] 14J depicts a vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products. Fields or options may be included that allow vendors to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU codes.

[0250] Figures 14K and 14L depict a dashboard Order Summary for displaying summary information about orders placed within the supply chain ecosystem. They present key order details such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity and enables stakeholders to track and manage orders efficiently.

[0251] Figure 14M depicts a customer subscription cart that allows a customer to add, change, or delete subscription plans. A list of selected subscriptions, pricing, and renewal dates can be displayed. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0252] FIG. 14N depicts a customer order tracking screen that allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment including processing, packing, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0253] FIG. 14O depicts a customer shipment tracking that provides customers with real-time tracking information on their shipments. Details such as carrier, tracking number, current location, estimated delivery date, etc. Customers can stay informed regarding the whereabouts of their shipments.

[0254] FIG. 14P depicts a Customer Subscription History that presents a historical record of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0255] Figure 14Q depicts the customer subscription change dialog, which allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0256] The depicted UI screens are not limiting. In some embodiments, the UI screens of Figures 14A through 14Q collectively represent the diverse functionality and features provided by the SPoG UI, providing participants with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0257] One or more computer systems may be configured to perform particular operations or actions by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes the system to perform the actions. One or more computer programs may be configured to perform particular operations or actions by containing instructions that, when executed by a data processing device, cause the device to perform the actions.

[0258] In one general aspect, a computer-implemented method may include integrating multiple communication channels (i.e., touch points) among a population of users into a unified interactive interface within a computer system, the unified interactive interface referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality of the population of users, the SPoG UI being positioned to facilitate operations across a supply chain ecosystem. The computer-implemented method may further include utilizing the SPoG UI to manage an end-to-end lifecycle of user interactions. The method may further include collecting data from the user interactions within the SPoG UI. Additionally, the method may include analyzing the collected data to generate one or more forecasts for business growth. The method may further include executing one or more artificial intelligence and / or machine learning algorithms to improve business operations based on the analyzed data. The method may further include incorporating periodic updates and improvements to the SPoG UI based on the analyzed data. The method may further include groups having distributors, resellers, customers, end customers, vendors, suppliers, where the population of users may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0259] Implementations may include one or more of the following features: The integration may include establishing communication links with multiple pre-existing business platforms; The aggregated interaction points may include one or more of websites, customer relationship management systems, vendor platforms, and supply chain management systems; The managing the end-to-end lifecycle may include one or more of initial contact, service fulfillment, and follow-up interactions; The collecting data may include monitoring and / or logging user activity within the SPoG UI; The analyzing the collected data is performed using advanced statistical algorithms; The artificial intelligence and machine learning algorithms include predictive analytics to identify market trends; The artificial intelligence and machine learning algorithms include a recommendation system to personalize user interactions; The improvements may be based on the analytics and / or analyzed user feedback received through the SPoG UI. Implementations of the described techniques may include hardware, methods or processes, or computer tangible media.

[0260] In one general aspect, the system may include a communication integration module configured to integrate multiple communication channels (i.e., touch points). The system may further include an aggregation module configured to combine the integrated communication channels into a unified interactive interface, referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality of a population of users, the SPoG UI being arranged to facilitate operations across a supply chain ecosystem. The system may further include a lifecycle management module configured to manage an end-to-end lifecycle of user interactions within the SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within the SPoG. The system may further include a data analysis module configured to generate one or more forecasts based on the collected data. The system may further include an artificial intelligence module configured to execute one or more AI and / or ML algorithms based on the analyzed data. The system may further include groups having distributors, resellers, customers, end customers, vendors, suppliers, where the user population may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0261] A system and method for automating SKU management may include a user interface configured to receive catalog files in two or more diverse formats from a user; a catalog transformation module configured to convert the catalog file into a standard format and predict a category taxonomy and attribute mapping for at least one catalog item, where the catalog transformation module utilizes iterative learning to suggest category taxonomy and attribute mapping; a Real-Time Data Mesh (RTDM) module for ensuring real-time data exchange and synchronization across system components, where the RTDM module facilitates real-time interaction between a user and the system; a Master Data Governance (MDG) module for validating the converted catalog and communicating any errors back to the vendor; and a Global Data Repository (GDR) for storing the validated catalog and maintaining data integrity of the stored catalog.

[0262] The system and method may include a search platform for indexing and searching the stored catalog. The user interface is a single pane of glass user interface (SPoG UI). A dynamic SKU creation module for generating SKUs for catalog items in an ERP system, where the catalog items represent one or more non-transactional products, and generating the SKU allows one or more products to be added to a customer's cart. The GDR is integrated with the RTDM module to support real-time data synchronization across systems. The MDG module generates a display indicating one or more errors made in the catalog conversion process associated with the one or more converted catalogs. A global pricing engine (GPE) for determining real-time pricing for the catalog items. The GPE determines the price of the product based on one or more of vendor price files, market trends, and historical pricing data. Implementations may include one or more of the following features: The interactive elements include links to various business platforms. The user interactions include actions may include one or more of click, hover, and input data. The collected data is analyzed using advanced statistical algorithms. The personalized content is generated based on recommendation algorithms. The method may further include updating the SPoG UI based on the user feedback and the data analysis results. Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.

[0263] It is understood that the intention is to use the Detailed Description, and not the Abstract, to interpret the claims. The Abstract may set forth one or more, but not all, exemplary embodiments of the invention as contemplated by the inventors, and is thus not intended to limit the invention and the appended claims in any manner.

[0264] The present invention has been described above with the aid of functional building blocks illustrating the implementation of certain functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for convenience of description. Alternative boundaries can be defined so long as the certain functions and relationships thereof are appropriately performed.

[0265] The foregoing description of specific embodiments fully reveals the general nature of the present invention, and by applying the knowledge of those skilled in the art, such specific embodiments can be easily modified and / or applied to various applications without departing from the general concept of the present invention without undue experimentation. Such applications and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It should be understood that the expressions or terms used herein are for the purpose of description rather than limitation, and thus the terms or terms used herein should be interpreted in light of the teachings and guidance provided by those skilled in the art.

[0266] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. 1. A system for automating SKU management, comprising: a user interface configured to receive catalog files of two or more different formats from a user; a catalog transformation module configured to convert the catalog file into a standard format and predict a category classification and attribute mapping for at least one catalog item, the catalog transformation module utilizing iterative learning to propose category classifications and attribute mappings; a real-time data mesh (RTDM) module for ensuring real-time data exchange and synchronization across the system components, the RTDM module facilitating real-time interaction between the users and the system; a Master Data Governance (MDG) module for validating the converted catalog and communicating any errors back to the vendor; a Global Data Repository (GDR) for storing said validated catalog and for maintaining data integrity of said stored catalog; and a search platform for indexing and searching the stored catalog.

2. The system of claim 1 , wherein the user interface is a single pane of glass user interface (SPoG UI).

3. 10. The system of claim 1, further comprising a dynamic SKU creation module for generating an SKU for the catalog item in an ERP system, the catalog item representing one or more non-transactional products, and generating the SKU allows the one or more products to be added to a customer's cart.

4. The system of claim 1 , wherein the GDR is integrated with the RTDM module to support real-time data synchronization across the system.

5. The system of claim 1 , wherein the MDG module generates a display indicating one or more errors incurred in the catalog conversion process associated with one or more converted catalogs.

6. The system of claim 1 , further comprising a global pricing engine (GPE) for determining real-time pricing for the catalog items.

7. The system of claim 6 , wherein the GPE determines the price of a product based on one or more of a vendor price file, market trends, and historical pricing data.

8. 1. A computerized method for automating SKU management, the method comprising: receiving two or more catalog files of various formats from a user through a user interface; converting the catalog file into a standard format using a catalog conversion module and using iterative learning to predict category classifications and attribute mappings for at least one catalog item; using a real-time data mesh (RTDM) module to ensure real-time data exchange and synchronization across the system components to facilitate real-time interaction between the users and the system; using a Master Data Governance (MDG) module to validate the converted catalog and communicate any errors back to the vendor; storing said validated catalog using a global data repository (GDR) and maintaining data integrity of said stored catalog; indexing and searching said stored catalog using a search platform; generating an SKU for the catalog item in an ERP system, the catalog item representing one or more non-transactional products, and allowing the one or more products to be added to a customer's cart using a dynamic SKU creation module; and determining real-time pricing for the catalog items using a global pricing engine (GPE), the price being determined based on one or more of vendor price files, market trends, and historical pricing data.

9. The computerized method of claim 8 , wherein the user interface is a single pane of glass user interface (SPoG UI).

10. 10. The computerized method of claim 8, further comprising: generating an SKU for the catalog item in an ERP system, the catalog item representing one or more non-transactional products, and generating the SKU allows the one or more products to be added to a customer's cart.

11. 10. The computerized method of claim 8, wherein the Global Data Repository (GDR) is integrated with the Real Time Data Mesh (RTDM) module to support real time data synchronization across the system.

12. 10. The computerized method of claim 8, wherein the master data governance (MDG) module generates an indication indicating one or more errors made in the catalog conversion process associated with the one or more converted catalogs.

13. 10. The computerized method of claim 8, further comprising determining real-time pricing for the catalog items using a global pricing engine (GPE).

14. 14. The computerized method of claim 13, wherein the GPE determines the price of a product based on one or more of a vendor price file, market trends, and historical pricing data.

15. A non-transitory computer-readable medium having a plurality of instructions stored thereon, the plurality of instructions, when executed by a processor, causing the processor to: receiving, from a user through a user interface, two or more catalog files in various formats; converting the catalog file to a standard format using a catalog conversion module and predicting category classifications and attribute mappings for at least one catalog item using iterative learning; using a real-time data mesh (RTDM) module to ensure real-time data exchange and synchronization across said system components to facilitate real-time interaction between said users and said system; using a Master Data Governance (MDG) module to validate the converted catalog and communicate any errors back to the vendor; storing said validated catalog using a global data repository (GDR) and maintaining data integrity of said stored catalog; indexing and searching said stored catalog using a search platform; generating an SKU for the catalog item in an ERP system, the catalog item representing one or more non-transactional products, using a dynamic SKU creation module to allow the one or more products to be added to a customer's cart; and determining real-time pricing for the catalog items using a global pricing engine (GPE), the price being determined based on one or more of vendor price files, market trends, and historical pricing data.

16. The computer-readable medium of claim 15 , wherein the user interface is a single pane of glass user interface (SPoG UI).

17. 16. The computer-readable medium of claim 15, further comprising generating an SKU for the catalog item in an ERP system, the catalog item representing one or more non-transactional products, and generating the SKU allows the one or more products to be added to a customer's cart.

18. 16. The computer-readable medium of claim 15, wherein the Global Data Repository (GDR) is integrated with the Real Time Data Mesh (RTDM) module to support real-time data synchronization across the system.

19. 16. The computer-readable medium of claim 15, wherein the master data governance (MDG) module generates an indication indicating one or more errors made in the catalog transformation process associated with the one or more transformed catalogs.

20. 16. The computer-readable medium of claim 15, further comprising determining real-time pricing for the catalog items using a global pricing engine (GPE), the GPE determining the price of a product based on one or more of vendor price files, market trends, and historical pricing data.

Images